Select features
interesting_features <- c(
"date", "county", "cases", "state",
"total_population", "num_deaths", "percent_smokers",
"percent_vaccinated", "income_ratio",
"population_density_per_sqmi", "percent_fair_or_poor_health",
"percent_unemployed_CHR", "violent_crime_rate",
"chlamydia_rate", "teen_birth_rate"
)
covid_data <- covid_data %>%
select(all_of(interesting_features)) %>%
mutate(date = as.Date(date)) # change date from character to "Date" class
# check the descriptive stats of the data frame
summary(covid_data)
## date county cases state
## Min. :2020-01-21 Length:790312 Min. : 1 Length:790312
## 1st Qu.:2020-06-01 Class :character 1st Qu.: 29 Class :character
## Median :2020-08-03 Mode :character Median : 174 Mode :character
## Mean :2020-08-02 Mean : 1586
## 3rd Qu.:2020-10-04 3rd Qu.: 768
## Max. :2020-12-04 Max. :430713
##
## total_population num_deaths percent_smokers percent_vaccinated
## Min. : 76 Min. : 32 Min. : 5.909 Min. : 4.0
## 1st Qu.: 12483 1st Qu.: 235 1st Qu.:14.982 1st Qu.:37.0
## Median : 27989 Median : 497 Median :17.021 Median :44.0
## Mean : 111580 Mean : 1425 Mean :17.488 Mean :42.2
## 3rd Qu.: 75216 3rd Qu.: 1171 3rd Qu.:19.760 3rd Qu.:49.0
## Max. :10057155 Max. :84296 Max. :41.491 Max. :66.0
## NA's :17835 NA's :74407 NA's :17835 NA's :20649
## income_ratio population_density_per_sqmi percent_fair_or_poor_health
## Min. : 2.543 Min. : 0.038 Min. : 8.121
## 1st Qu.: 4.016 1st Qu.: 19.559 1st Qu.:14.361
## Median : 4.406 Median : 47.951 Median :17.260
## Mean : 4.520 Mean : 240.901 Mean :17.953
## 3rd Qu.: 4.874 3rd Qu.: 129.528 3rd Qu.:20.924
## Max. :11.971 Max. :28069.676 Max. :40.991
## NA's :18326 NA's :17835 NA's :17835
## percent_unemployed_CHR violent_crime_rate chlamydia_rate teen_birth_rate
## Min. : 1.302 Min. : 0.0 Min. : 35.8 Min. : 2.11
## 1st Qu.: 3.151 1st Qu.: 121.3 1st Qu.: 230.6 1st Qu.: 18.93
## Median : 3.885 Median : 209.7 Median : 332.3 Median : 28.15
## Mean : 4.135 Mean : 256.0 Mean : 404.6 Mean : 29.71
## 3rd Qu.: 4.815 3rd Qu.: 340.6 3rd Qu.: 505.0 3rd Qu.: 38.97
## Max. :19.904 Max. :1819.5 Max. :6120.3 Max. :103.05
## NA's :17835 NA's :61877 NA's :45400 NA's :45172
Exploratory Data Analysis (EDA)
Table of COVID-19 prevalence for every state
library(lemon)
##
## Attaching package: 'lemon'
## The following object is masked from 'package:purrr':
##
## %||%
## The following objects are masked from 'package:ggplot2':
##
## CoordCartesian, element_render
knit_print.data.frame <- lemon_print
covid_prevalence_table_state <- covid_data %>%
# The following lines are for calculating daily growth rate
group_by(state, date) %>%
summarize(cases = sum(cases),
population = mean(total_population, na.rm=TRUE)) %>%
mutate(cases_growth_rate = (cases - lag(cases) / lag(cases))) %>%
# The following lines are for group_by values for each state
group_by(state) %>%
summarize(total_cases = max(cases),
total_cases_per_capita = total_cases / mean(population, na.rm=TRUE),
mean_cases_growth_rate = mean(cases_growth_rate, na.rm=TRUE)) %>%
arrange(desc(total_cases))
head(data.frame(covid_prevalence_table_state))
| Texas |
1322711 |
4.1147229 |
399154.6 |
| California |
1318139 |
1.1911306 |
401735.0 |
| Florida |
1036294 |
3.1404685 |
381475.3 |
| Illinois |
771696 |
0.8482987 |
184156.4 |
| New York |
690143 |
1.3648911 |
373401.0 |
| Georgia |
473343 |
5.0353201 |
168740.8 |
tail(data.frame(covid_prevalence_table_state))
| 49 |
District of Columbia |
22480 |
0.0341118 |
10817.9485 |
| 50 |
Hawaii |
18373 |
0.0492675 |
5825.6410 |
| 51 |
Maine |
12833 |
0.1481734 |
3959.1199 |
| 52 |
Vermont |
4755 |
0.1025763 |
1440.5074 |
| 53 |
Virgin Islands |
1613 |
NaN |
683.7438 |
| 54 |
Northern Mariana Islands |
106 |
NaN |
67.8951 |
There are NAs in the table because there are missing values for the population in that county/state in the dataset.
Table of COVID-19 prevalence for every county
covid_prevalence_table_county <- covid_data %>%
# The following lines are for calculating daily growth rate
group_by(county, date) %>%
summarize(cases = sum(cases),
population = mean(total_population, na.rm=TRUE)) %>%
mutate(cases_growth_rate = (cases - lag(cases) / lag(cases))) %>%
# The following lines are for group_by values for each state
group_by(county) %>%
summarize(total_cases = max(cases),
total_cases_per_capita = total_cases / mean(population, na.rm=TRUE),
mean_cases_growth_rate = mean(cases_growth_rate, na.rm=TRUE)) %>%
arrange(desc(total_cases))
head(data.frame(covid_prevalence_table_county))
| Los Angeles |
430713 |
0.0428265 |
140152.12 |
| New York City |
329406 |
0.0389279 |
200729.30 |
| Cook |
323162 |
0.1204322 |
94207.00 |
| Miami-Dade |
238812 |
0.0896301 |
96144.40 |
| Maricopa |
224924 |
0.0550132 |
72659.49 |
| Harris |
196640 |
0.0827030 |
73330.53 |
tail(data.frame(covid_prevalence_table_county))
| 1923 |
Daggett |
9 |
0.0119840 |
0.7058824 |
| 1924 |
Petroleum |
8 |
0.0179775 |
3.2833333 |
| 1925 |
Borden |
5 |
0.0071633 |
1.6111111 |
| 1926 |
Lake and Peninsula Borough |
5 |
0.0035436 |
3.4340659 |
| 1927 |
Tinian |
2 |
NaN |
0.4615385 |
| 1928 |
Loving |
1 |
0.0131579 |
0.0000000 |
Visualization 1 - distributions of numeric features
covid_data_group_by_sate <- covid_data %>%
group_by(state) %>%
summarize(
num_deaths = max(num_deaths),
percent_smokers = mean(percent_smokers, na.rm=TRUE),
percent_vaccinated = max(percent_vaccinated),
income_ratio = mean(income_ratio, na.rm=TRUE),
population_density_per_sqmi = mean(population_density_per_sqmi,
na.rm=TRUE),
percent_fair_or_poor_health = mean(percent_fair_or_poor_health,
na.rm=TRUE),
percent_unemployed_CHR = mean(percent_unemployed_CHR, na.rm=TRUE),
violent_crime_rate = mean(violent_crime_rate, na.rm=TRUE),
chlamydia_rate = mean(chlamydia_rate, na.rm=TRUE),
teen_birth_rate = mean(teen_birth_rate, na.rm=TRUE)
) %>%
merge(covid_prevalence_table_state, by="state") %>%
arrange(desc(total_cases))
par(mfrow=c(3, 4))
covid_data_group_by_sate_long <- covid_data_group_by_sate %>%
select_if(is.numeric) %>%
pivot_longer(everything())
covid_data_group_by_sate_long %>%
ggplot(aes(x=value)) +
geom_density(fill='grey') +
facet_wrap(~name, scales='free') +
theme(strip.text = element_text(size=7),
axis.text.x = element_text(size=5),
axis.text.y = element_text(size=5),
plot.title = element_text(hjust = 0.5)) +
labs(title="Density plots of numeric feature",
x ="",
y = "Density")
Visualization 2 - relationships between total COVID-19 cases per capita of each state and other features
par(mfrow=c(3, 4))
covid_data_group_by_sate_long <- covid_data_group_by_sate %>%
select_if(is.numeric) %>%
pivot_longer(-total_cases_per_capita)
case_per_capita_plot <- covid_data_group_by_sate_long %>%
ggplot(aes(x=value, y=total_cases_per_capita)) +
geom_point(alpha = 0.4) +
facet_wrap(~name, scales='free') +
theme(strip.text = element_text(size=7),
axis.text.x = element_text(size=7),
axis.text.y = element_text(size=7),
plot.title = element_text(hjust = 0.5),
panel.spacing = unit(2.5, "lines")) +
labs(title="Plots of total COVID-19 cases per capita v.s. other features",
x ="",
y = "COVID-19 cases per capita") +
scale_x_continuous(labels = scales::label_number_si())
fig_case_per_capita_plot <- ggplotly(case_per_capita_plot)
fig_case_per_capita_plot
Visualization 3 - relationships between average COVID-19 cases growth rate for each state and other features
par(mfrow=c(3, 4))
covid_data_group_by_sate_long <- covid_data_group_by_sate %>%
select_if(is.numeric) %>%
pivot_longer(-mean_cases_growth_rate)
covid_growth_rate_plot <- covid_data_group_by_sate_long %>%
ggplot(aes(x=value, y=mean_cases_growth_rate)) +
geom_point(alpha = 0.4) +
facet_wrap(~name, scales='free') +
theme(strip.text = element_text(size=7),
axis.text.x = element_text(size=7),
axis.text.y = element_text(size=7),
plot.title = element_text(hjust = 0.5),
panel.spacing = unit(2.5, "lines")) +
labs(title="Plots of average COVID-19 growth rate v.s. other features",
x ="",
y = "COVID-19 growth rate") +
scale_x_continuous(labels = scales::label_number_si()) +
scale_y_continuous(labels = scales::label_number_si())
fig_covid_growth_rate_plot <- ggplotly(covid_growth_rate_plot)
fig_covid_growth_rate_plot